The DynAIRx project protocol: artificial intelligence for dynamic prescribing optimisation and care integration in multimorbidity

Walker, L. E. et al. (2022) The DynAIRx project protocol: artificial intelligence for dynamic prescribing optimisation and care integration in multimorbidity. Journal of Multimorbidity and Comorbidity, 12, p. 26335565221145493. (doi: 10.1177/26335565221145493) (PMID:36545235) (PMCID:PMC9761229)

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Background: Structured Medication Reviews (SMRs) are intended to help deliver the NHS Long Term Plan for medicines optimisation in people living with multiple long-term conditions and polypharmacy. It is challenging to gather the information needed for these reviews due to poor integration of health records across providers and there is little guidance on how to identify those patients most urgently requiring review. Objective: To extract information from scattered clinical records on how health and medications change over time, apply interpretable artificial intelligence (AI) approaches to predict risks of poor outcomes and overlay this information on care records to inform SMRs. We will pilot this approach in primary care prescribing audit and feedback systems, and co-design future medicines optimisation decision support systems. Design: DynAIRx will target potentially problematic polypharmacy in three key multimorbidity groups, namely, people with (a) mental and physical health problems, (b) four or more long-term conditions taking ten or more drugs and (c) older age and frailty. Structured clinical data will be drawn from integrated care records (general practice, hospital, and social care) covering an ∼11m population supplemented with Natural Language Processing (NLP) of unstructured clinical text. AI systems will be trained to identify patterns of conditions, medications, tests, and clinical contacts preceding adverse events in order to identify individuals who might benefit most from an SMR. Discussion: By implementing and evaluating an AI-augmented visualisation of care records in an existing prescribing audit and feedback system we will create a learning system for medicines optimisation, co-designed throughout with end-users and patients.

Item Type:Articles
Additional Information:DynAIRx has been funded by the National Institute for Health and Care Research Artificial Intelligence for Multiple Long-Term Conditions (AIM) call (NIHR 203986). BRIT2 is supported by funding from the National Institute for Health and Care Research (Cluster randomised trial to improve antibiotic prescribing in primary care: individualised knowledge support during consultation for general practitioners and patients: Grant number NIHR130581) and Health Data Research UK (Better Care Northern Partnership, better antibiotic prescribing in frail elderly people with polypharmacy: learning from practice and nudging prescribers into better practices).
Keywords:Multimorbidity, polypharmacy, frailty, mental health, artificial intelligence, medicines optimisation.
Glasgow Author(s) Enlighten ID:Mair, Professor Frances
Authors: Walker, L. E., Abuzour, A. S., Bollegala, D., Clegg, A., Gabbay, M., Griffiths, A., Kullu, C., Leeming, G., Mair, F. S., Maskell, S., Relton, S., Ruddle, R. A., Shantsila, E., Sperrin, M., Van Staa, T., Woodall, A., and Buchan, I.
College/School:College of Medical Veterinary and Life Sciences > School of Health & Wellbeing > General Practice and Primary Care
Journal Name:Journal of Multimorbidity and Comorbidity
Publisher:SAGE Publications
ISSN (Online):2633-5565
Published Online:15 December 2022
Copyright Holders:Copyright © 2022 The Authors
First Published:First published in Journal of Multimorbidity and Comorbidity 12: 26335565221145493
Publisher Policy:Reproduced under a Creative Commons License

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